Response to 'Predicting the diagnosis of autism spectrum disorder using gene pathway analysis'.
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ABSTRACT:
SUBMITTER: Robinson EB
PROVIDER: S-EPMC4113933 | biostudies-literature | 2014 Aug
REPOSITORIES: biostudies-literature
Publications
Response to 'Predicting the diagnosis of autism spectrum disorder using gene pathway analysis'.
Robinson E B EB Howrigan D D Yang J J Ripke S S Anttila V V Duncan L E LE Jostins L L Barrett J C JC Medland S E SE MacArthur D G DG Breen G G O'Donovan M C MC Wray N R NR Devlin B B Daly M J MJ Visscher P M PM Sullivan P F PF Neale B M BM
Molecular psychiatry 20131022 8
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Project description:Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs) of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify affected cellular processes and develop a diagnostic test. This test was then applied to two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of 237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases. This classifier also predicted 84.3% of cases in an ethnically related Tuscan cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1, GRM5) had the largest effect in the classifier with some acting as vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI (ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an accurate diagnostic test for a genetically homogeneous group to aid in early detection of ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular processes common to ASD across ethnicities. Our results have wide implications for detection, intervention and prevention of ASD.
| S-EPMC3966080 | biostudies-literature
Project description:ObjectiveThe aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD.MethodsWe used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value ＜0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learning algorithms.ResultsHierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively.ConclusionThe results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy.
| S-EPMC5290715 | biostudies-literature
Project description:BackgroundMany adults with autism spectrum disorder (ASD) remain undiagnosed. Specialist assessment clinics enable the detection of these cases, but such services are often overstretched. It has been proposed that unnecessary referrals to these services could be reduced by prioritizing individuals who score highly on the Autism-Spectrum Quotient (AQ), a self-report questionnaire measure of autistic traits. However, the ability of the AQ to predict who will go on to receive a diagnosis of ASD in adults is unclear.MethodWe studied 476 adults, seen consecutively at a national ASD diagnostic referral service for suspected ASD. We tested AQ scores as predictors of ASD diagnosis made by expert clinicians according to International Classification of Diseases (ICD)-10 criteria, informed by the Autism Diagnostic Observation Schedule-Generic (ADOS-G) and Autism Diagnostic Interview-Revised (ADI-R) assessments.ResultsOf the participants, 73% received a clinical diagnosis of ASD. Self-report AQ scores did not significantly predict receipt of a diagnosis. While AQ scores provided high sensitivity of 0.77 [95% confidence interval (CI) 0.72-0.82] and positive predictive value of 0.76 (95% CI 0.70-0.80), the specificity of 0.29 (95% CI 0.20-0.38) and negative predictive value of 0.36 (95% CI 0.22-0.40) were low. Thus, 64% of those who scored below the AQ cut-off were 'false negatives' who did in fact have ASD. Co-morbidity data revealed that generalized anxiety disorder may 'mimic' ASD and inflate AQ scores, leading to false positives.ConclusionsThe AQ's utility for screening referrals was limited in this sample. Recommendations supporting the AQ's role in the assessment of adult ASD, e.g. UK NICE guidelines, may need to be reconsidered.
| S-EPMC4988267 | biostudies-literature
Project description:Autism spectrum disorder (ASD) is characterised by the concomitant occurrence of impaired social interaction; restricted, perseverative and stereotypical behaviour; and abnormal communication skills. Recent epidemiological studies have reported a dramatic increase in the prevalence of ASD with as many as 1 in every 59 children being diagnosed with ASD. The fact that ASD appears to be principally genetically driven, and may be reversible postnatally, has raised the exciting possibility of using gene therapy as a disease-modifying treatment. Such therapies have already started to seriously impact on human disease and particularly monogenic disorders (e.g. metachromatic leukodystrophy, SMA type 1). In regard to ASD, technical advances in both our capacity to model the disorder in animals and also our ability to deliver genes to the central nervous system (CNS) have led to the first preclinical studies in monogenic ASD, involving both gene replacement and silencing. Furthermore, our increasing awareness and understanding of common dysregulated pathways in ASD have broadened gene therapy's potential scope to include various polygenic ASDs. As this review highlights, despite a number of outstanding challenges, gene therapy has excellent potential to address cognitive dysfunction in ASD.
| S-EPMC6011246 | biostudies-literature
Project description:Autism spectrum disorders (ASD) are clinically heterogeneous and biologically complex. In general it remains unclear, what biological factors lead to changes in the brains of autistic individuals. A considerable number of transcriptome analyses have been performed in attempts to address this question, but their findings lack a clear consensus. As a result, each of these individual studies has not led to any significant advance in understanding the autistic phenotype as a whole. Here, we report a meta-analysis of more than 1000 microarrays across twelve independent studies on expression changes in ASD compared to unaffected individuals, in both blood and brain tissues. We identified a number of known and novel genes that are consistently differentially expressed across three studies of the brain (71 samples in total). A subset of the highly ranked genes is suggestive of effects on mitochondrial function. In blood, consistent changes were more difficult to identify, despite individual studies tending to exhibit larger effects than the brain studies. Our results are the strongest evidence to date of a common transcriptome signature in the brains of individuals with ASD.
| S-EPMC4933311 | biostudies-literature
Project description:The aim of this study was to identify ASD vulnerability components, explore possible vulnerability subgroups, and evaluate environmental contribution to phenotypic variability. We performed PCA and cluster analysis using ASD risk factor data. We found that PC1 had a higher correlation with psychosocial stress (maternal stress, maternal education, and social class), and PC2 a higher correlation with biological factors (Psychiatric Family History and Gestational Complications). Clustering analysis of PCA scores using bootstrap approach showed two clusters. Comparing the methylome between clusters we found 11.879 DMPs (p<0.05), and their CpG sites were enriched in VMRs, most showing environmental and genetic influences. Hypermethylated probes presented higher rates in different regulatory regions associated with functional SNPs, indicating that the subgroups may have different affected regulatory regions and their liability to disease explained by common variations. Vulnerability score moderated by epigenetic clock was associated with Vineland Total score (p=0.0036, adjR2=0.31), suggesting risk factors with stress burden can influence phenotype.
2021-10-01 | GSE164563 | GEO
Project description:Pivotal response treatment (PRT) is an evidence-based behavioral intervention based on applied behavior analysis principles aimed to improve social communication skills in individuals with autism spectrum disorder (ASD). PRT adopts a more naturalistic approach and focuses on using a number of strategies to help increase children's motivation during intervention. Since its conceptualization, PRT has received much empirical support for eliciting therapeutic gains in greater use of functional social communication skills in individuals with ASD. Building upon the empirical evidence supporting PRT, recent advancements have increasingly turned to using interdisciplinary research integrating neuroimaging techniques and behavioral measures to help identify objective biomarkers of treatment, which have two primary purposes. First, neuroimaging results can help characterize how PRT may elicit change, and facilitate partitioning of the heterogeneous profiles of neural mechanisms underlying similar profile of behavioral changes observed over PRT. Second, neuroimaging provides an objective means to both map and track how biomarkers may serve as reliable and sensitive predictors of responder profiles to PRT, assisting clinicians to identify who will most likely benefit from PRT. Together, a better understanding of both mechanisms of change and predictors of responder profile will help PRT to serve as a more precise and targeted intervention for individuals with ASD, thus moving towards the goal of precision medicine and improving quality of care. This review focuses on the recent emerging neuroimaging evidences supporting PRT, offering current perspectives on the importance of interdisciplinary research to help clinicians better understand how PRT works and predict who will respond to PRT.
| S-EPMC5488784 | biostudies-other
Project description:Background: As the demand for early and accurate diagnosis of autism spectrum disorder (ASD) increases, the integration of machine learning (ML) and explainable artificial intelligence (XAI) is emerging as a critical advancement that promises to revolutionize intervention strategies by improving both accuracy and transparency. Methods: This paper presents a method that combines XAI techniques with a rigorous data-preprocessing pipeline to improve the accuracy and interpretability of ML-based diagnostic tools. Our preprocessing pipeline included outlier removal, missing data handling, and selecting pertinent features based on clinical expert advice. Using R and the caret package (version 6.0.94), we developed and compared several ML algorithms, validated using 10-fold cross-validation and optimized by grid search hyperparameter tuning. XAI techniques were employed to improve model transparency, offering insights into how features contribute to predictions, thereby enhancing clinician trust. Results: Rigorous data-preprocessing improved the models' generalizability and real-world applicability across diverse clinical datasets, ensuring a robust performance. Neural networks and extreme gradient boosting models achieved the best performance in terms of accuracy, precision, and recall. XAI techniques demonstrated that behavioral features significantly influenced model predictions, leading to greater interpretability. Conclusions: This study successfully developed highly precise and interpretable ML models for ASD diagnosis, connecting advanced ML methods with practical clinical application and supporting the adoption of AI-driven diagnostic tools by healthcare professionals. This study's findings contribute to personalized intervention strategies and early diagnostic practices, ultimately improving outcomes and quality of life for individuals with ASD.
| S-EPMC11592605 | biostudies-literature
Project description:Autism is an etiologically heterogeneous group of neurodevelopmental disorders, diagnosed mostly by the clinical behavioral phenotypes. The concept that the tumor-related gene PTEN plays a critical role in autism spectrum disorder has emerged over the last decade. In this review, we focus on the essential role of the PTEN signaling pathway in neuronal differentiation and the formation of neural circuitry, as well as genetic mouse models with Pten manipulations. Particularly, accumulated data suggest that the effect of PTEN on neural stem-cell development contributes significantly to the pathophysiology of autism spectrum disorders.
| S-EPMC5562549 | biostudies-other
Project description:Bipolar disorder (BPD) and schizophrenia (SCH) show similar neuropsychiatric behavioral disturbances, including impaired social interaction and communication, seen in autism spectrum disorder (ASD) with multiple overlapping genetic and environmental influences implicated in risk and course of illness. GeneAnalytics software was used for pathway analysis and genetic profiling to characterize common susceptibility genes obtained from published lists for ASD (792 genes), BPD (290 genes) and SCH (560 genes). Rank scores were derived from the number and nature of overlapping genes, gene-disease association, tissue specificity and gene functions subdivided into categories (e.g., diseases, tissues or functional pathways). Twenty-three genes were common to all three disorders and mapped to nine biological Superpathways including Circadian entrainment (10 genes, score = 37.0), Amphetamine addiction (five genes, score = 24.2), and Sudden infant death syndrome (six genes, score = 24.1). Brain tissues included the medulla oblongata (11 genes, score = 2.1), thalamus (10 genes, score = 2.0) and hypothalamus (nine genes, score = 2.0) with six common genes (BDNF, DRD2, CHRNA7, HTR2A, SLC6A3, and TPH2). Overlapping genes impacted dopamine and serotonin homeostasis and signal transduction pathways, impacting mood, behavior and physical activity level. Converging effects on pathways governing circadian rhythms support a core etiological relationship between neuropsychiatric illnesses and sleep disruption with hypoxia and central brain stem dysfunction.
| S-EPMC5372543 | biostudies-literature